Data Platform Engineer II (Contract)

About us:

Spring Financial is a Canadian financial technology company focused on making every day financial services simpler, faster, and more accessible.

We build technology that helps Canadians build credit, save money, and access lending products without unnecessary friction. Our platforms allow customers to apply and manage their finances online, by text, or over the phone, making the experience convenient and flexible.

Since launching in 2014, Spring has grown into one of Canada’s largest fintechs, with over 250,000+ product originations across credit-building products, personal lending, and mortgage solutions. We’re a fast-growing, product-driven team that values practical solutions, strong execution, and thoughtful collaboration. We give people ownership, trust them to make decisions, and focus on building systems that scale reliably.

If you’re interested in working on real-world fintech platforms used by hundreds of thousands of Canadians, Spring offers the opportunity to make a tangible impact through well-built technology.
About the role:

As a Data Platform Engineer II at Spring, you are a hands-on builder who delivers reliable, scalable data infrastructure. You'll work across our core stack of AWS, Snowflake, dbt, and Airflow to build pipelines and platform components that are secure, observable, and aligned with business needs. You'll own meaningful pieces of our data platform end to end: scoping the work, making the call on implementation, and shipping it. You'll make practical use of AI in your day-to-day engineering (e.g., for testing, schema discovery, and code generation) and contribute to AI-enabled features in the platform itself (e.g., automated QA, anomaly detection). Working with partners across Finance, Risk, Product, Analytics, and Engineering, you'll help clarify requirements, set and meet SLAs, and make sure the data you deliver is trustworthy.

What you’ll do:
Platform & Pipeline Engineering

- Build and maintain scalable, secure, and reliable data pipelines and platform components across AWS and Snowflake

- Help scope and deliver initiatives that modernize legacy data flows, bring together batch and streaming sources, and enable self-serve analytics across the organization.

- Apply and help improve our engineering standards around testing, observability, security, and CI/CD within data systems.

AI Integration

- Help build AI capabilities into data pipelines (e.g., anomaly detection, automated tagging) and use AI in your own development practices (e.g., assisted testing, documentation).

- Stay curious about how agentic and AI-driven workflows are changing data platform requirements, and bring what you learn back to the team.

Cross-Functional Partnership

- Work with engineers and business partners to clarify requirements, surface risks early, and propose practical solutions.

- Partner with Analytics, ML, Finance, and other business teams to deliver data with the latency, accuracy, and governance their use cases need.

- Communicate clearly about your work and its constraints, and be comfortable explaining technical trade-offs to non-technical partners.

Craft & Growth

- Contribute to technical design discussions and code reviews, and to the ongoing evolution of our data architecture.

- Take ownership of the problems you pick up, holding a high bar for quality, documentation, and engineering craft, and seeing work through to production.

- Sharpen your technical judgment through code review and design discussion with peers across data, software, and analytics engineering, and share what you know in return.

What we're looking for:
Requirements

- Solid experience building data pipelines using Snowflake and AWS-native tools (e.g., Glue, Lambda, Redshift, Step Functions)

- Working experience with real-time data systems such as Kafka, Kinesis, Flink, or Spark Streaming

- Good working knowledge of data modeling, schema evolution, and secure, privacy-conscious data design

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